Co-build the training pipeline for a frontier world model.
We embed an engineer in your team. Your model wraps as an index. Reproducible slices, audit logs, deterministic exports.
Talk to us
World Model Data
Program in market · Partner-first
Training data infrastructure for the physical AI era.
World models, robotics, and autonomy don't need another upload tool.
They need structured video at scale — with provenance.
PB-scaleMulti-million-hour corpora across cloud and partner sources
Lab-gradeReproducibility, deduplication, quality scoring per clip
ProvenanceSource, license, capture context — every clip carries the trail
The problem
Getting from raw footage to training-ready data shouldn't take a quarter.
World model and physical AI pipelines need scale, structure, and provenance — and most teams build it by hand.
Scale problem.
Multi-million-hour corpora crack internal pipelines.
Structure problem.
Models need scenes, events, quality tiers. Upload tools don't do that.
Provenance problem.
Source, license, capture context — every clip needs a paper trail.
Who needs this
Built for teams training models
Built for teams training models
on the physical world.
Partnership-first. Data infrastructure partner, not a competing model lab.
World model labs.
Curated, structured video at the scale and quality model training requires.
Robotics & autonomy.
Filter for the events, scenes, and edge cases that matter to the policy.
Simulation.
Ground simulation against real-world video
— searchable, structured.
Video data providers.
Productize raw footage as queryable, licensable datasets.
Research consortia.
Multi-party datasets with consistent structure, access controls, provenance.
Internal data platforms.
Replace bespoke labeling and curation with one platform.
Capabilities
From raw footage to training-ready data.
The pipeline the modeling team would otherwise build by hand
— standardised, reproducible, audited.
Petabyte ingest.
Files, datasets, RTSP captures — with throughput tuned for corpus-scale pipelines.
Quality scoring.
Per-clip quality, dedup, near-duplicate detection. Train on what's worth training on.
Scene segmentation.
Reproducible scene + event boundaries you can slice the corpus against.
Event labeling.
Bring your taxonomy. Indexes as code — your labeling model wraps cleanly.
Provenance trail.
Source, license, capture context attached to every clip
— immutable.
Lab-grade reproducibility.
Versioned slices, run logs, deterministic exports. Auditable training runs.
The shortest path
Bring your corpus. Get a queryable, training-ready dataset.
The data infra you would otherwise build
— configured for your scenes, events, and provenance schema.
videodb dataset create --schema robotics.yml --source s3://your-corpus/
For research & partner programs
Research track
Partner track
Productize your corpus as a queryable,
Two tracks for the world-model wedge.
A research track for labs and a partner track for video data providers and sovereign clouds.
Productize your corpus as a queryable,
licensable dataset.
VideoDB sits on your hosting as the structured-video layer. Sovereign cloud partnership in market.
Read the brief